Medical diagnostic imaging is increasingly relied upon to detect various physical maladies. For example, X-ray-based Computerized Tomography (CT) is particularly suited for detection of skeletal injuries and dense cancerous lesions. As another example, Magnetic Resonance Imaging (MRI) is particularly well suited for detecting soft tissue conditions such as strokes, aneurysms and arterial blockages.
While advances in MRI and CT diagnostic systems have reduced the amount of time necessary, clinical diagnostic imaging still requires a significant amount of time to perform a detailed scan of a region of interest of a patient. Once the detailed scan is obtained, an expert clinician must interpret the results, often challenged by inter-patient and intra-patient scan variations. In particular, the expert clinician seeks to recognize anatomical landmarks and to identify departures from a normal tissue structure by having the scan spatially oriented and normalized for an easier comparison to a previously taken scan for the same patient or perhaps to other patients. It would be further desirable that such orientation would be normalized even between different imaging modalities (e.g., MRI, CT) so these results may assist in future diagnostic procedures for the patient.
Consequently, a significant need exists for an automated approach to performing cranial scout scans that identify roll, yaw and pitch in order that corrections relative to a standard may be accomplished so that useful comparisons may be made with other head scans for the same patient or other patients.